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A Student’s Guide to Bayesian Statistics
by
Ben Lambert
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Student Resources
How to best use this book
The subjective worlds of Frequentist and Bayesian statistics
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Answers to Problem Sets
Probability - the nuts and bolts of Bayesian inference
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Likelihoods
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Priors
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The devil is in the denominator
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The posterior - the goal of Bayesian inference
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An introduction to distributions for the mathematically uninclined
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Conjugate priors
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Evaluation of model fit and hypothesis testing
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Making Bayesian analysis objective?
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Leaving conjugates behind: Markov Chain Monte Carlo
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Random Walk Metropolis
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Gibbs sampling
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Hamiltonian Monte Carlo
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Stan
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Hierarchical models
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Linear regression models
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Generalised linear models and other animals
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The intuition behind the Hamiltonian Monte Carlo algorithm
Video of The intuition behind the Hamiltonian Monte Carlo algorithm
An introduction to Hamiltonian Monte Carlo
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U turns in posterior space: motivating the NUTS sampler
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